In today’s business landscape, the reliance on artificial intelligence (AI) and advanced data capabilities has never been more crucial. However, despite technological advancements, there is a growing dissatisfaction among business leaders with their IT departments.
A recent study by IBM’s Institute for Business Value has revealed a significant decline in confidence among top executives regarding the effectiveness of basic IT services. While AI offers transformative capabilities, particularly generative artificial intelligence (gen AI), challenges in data management pose a significant obstacle to realizing these benefits.
Many executives feel that inadequate data quality, accessibility, and security are exposing their businesses to unnecessary risks. This emerging data liability concern is prompting companies to take action to mitigate these risks.
The Shift in Expectations for IT Services
The expectations placed on IT departments have undergone a dramatic shift in recent years due to digital transformation and the widespread adoption of AI. Businesses are increasingly relying on technology to gain a competitive edge, placing immense pressure on IT departments to deliver. However, this increased pressure has not translated into greater confidence in IT services.
The IBM study indicates that only 43% of tech leaders believe their organizations are effective at delivering differentiated products and services. Additionally, only half of tech leaders feel that their teams have the knowledge and skills necessary to incorporate new technology. Specifically, when it comes to generative AI expertise, 40% of tech CxOs have reported increased anxiety over the past six months.
Concerns around Data Management
Data serves as the foundation for AI operations, but it also presents a significant liability if not managed properly. Data quality, accessibility, and security are essential for ensuring the smooth functioning of AI applications. Poor data management can lead to inaccuracies in models, biased outputs, and security vulnerabilities, all of which can have far-reaching consequences for businesses.
In fact, according to the IBM survey, 43% of business leaders have expressed growing concerns about their technology infrastructure due to gen AI over the past six months. Additionally, the increasing regulatory scrutiny around data collection, storage, and usage adds further pressure on companies to comply with regulations and data protection laws.
The Importance of Governance, Risk, and Compliance (GRC)
One solution to address the rising data liability concerns is the implementation of a robust governance, risk, and compliance (GRC) framework. GRC aligns IT practices with business objectives, ensuring that risks are managed effectively and regulatory compliance is maintained. By integrating GRC into IT operations, businesses can proactively tackle the challenges associated with data management and scaling AI.
The Three Pillars of the GRC Framework:
Governance: Establishes rules, policies, and processes to align corporate activities with business goals, ensuring that management can influence activities across the organization.
Risk Management: Identifies, assesses, and mitigates financial, legal, strategic, and security risks, crucial for addressing vulnerabilities in AI and data practices.
Compliance: Ensures adherence to internal and external regulations, keeping the organization in line with data privacy laws and regulations.
Leveraging GRC for Data Liability
One of the key benefits of GRC is its ability to elevate discussions around data management beyond the IT department and into the boardroom. By fostering collaboration between various departments, GRC ensures that data management is treated as a strategic priority rather than a technical issue.
GRC establishes clear policies for data governance, ensuring consistent handling of data across the organization. Implementing data fabric architectures and enterprise data standards facilitates the scaling of AI applications by breaking down data silos and enabling seamless data integration in real-time.
Addressing the Gender Gap in AI
An often overlooked aspect of data liability is the importance of diversity in AI development. Encouraging more women to pursue IT and AI roles can bring diverse perspectives to AI development and data management, safeguarding against biases in AI models. By expanding the talent pool, businesses can shape AI transformation in a more inclusive and representative manner.
The Role of GRC Software
Effective implementation of GRC requires the right tools, such as GRC software that can streamline processes like risk assessments, compliance management, and audits. These platforms offer centralized management of data governance, compliance tracking, and real-time risk assessment, reducing the administrative burden on IT teams and ensuring compliance with regulations.
GRC tools can also provide valuable insights by correlating data management practices with business outcomes, leading to higher revenue growth for organizations that connect technology investments with measurable results.
The Data Management Advantage
As businesses continue to embrace AI on a larger scale, effective data management is crucial for success. Data liability poses a significant risk for businesses, but by adopting a robust GRC framework, organizations can turn data management into a competitive advantage. GRC provides the necessary structure to govern data, manage risks, and ensure compliance, enabling businesses to thrive in an AI-driven future.
